library(beeswarm)
library(naniar)
library(zoo)

Attaching package: ‘zoo’

The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric
# install.packages("zoo")
library(janitor)

Attaching package: ‘janitor’

The following objects are masked from ‘package:stats’:

    chisq.test, fisher.test
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
# install.packages("GGally")
# library(sets)
library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ──────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
✓ ggplot2 3.3.3     ✓ purrr   0.3.4
✓ tibble  3.0.6     ✓ stringr 1.4.0
✓ tidyr   1.1.2     ✓ forcats 0.5.1
✓ readr   1.4.0     
── Conflicts ─────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
library(ggplot2)
library(GGally) # for ggpairs
Registered S3 method overwritten by 'GGally':
  method from   
  +.gg   ggplot2
library(lubridate)

Attaching package: ‘lubridate’

The following objects are masked from ‘package:base’:

    date, intersect, setdiff, union
# install.packages("maps")
# library(maps)
load_file <- function(file_path){
  read_csv(file_path)
}

tx_data <- load_file("./../data/COVID-19_cases_TX.csv")

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────
cols(
  county_fips_code = col_character(),
  county_name = col_character(),
  state = col_character(),
  state_fips_code = col_double(),
  date = col_date(format = ""),
  confirmed_cases = col_double(),
  deaths = col_double()
)
global_mobility_report <- load_file("./../data/Global_Mobility_Report.csv")

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────
cols(
  country_region_code = col_character(),
  country_region = col_character(),
  sub_region_1 = col_character(),
  sub_region_2 = col_logical(),
  metro_area = col_logical(),
  iso_3166_2_code = col_character(),
  census_fips_code = col_logical(),
  date = col_date(format = ""),
  retail_and_recreation_percent_change_from_baseline = col_double(),
  grocery_and_pharmacy_percent_change_from_baseline = col_double(),
  parks_percent_change_from_baseline = col_double(),
  transit_stations_percent_change_from_baseline = col_double(),
  workplaces_percent_change_from_baseline = col_double(),
  residential_percent_change_from_baseline = col_double()
)

4199216 parsing failures.
 row        col           expected                  actual                                   file
3036 metro_area 1/0/T/F/TRUE/FALSE Kabul Metropolitan Area './../data/Global_Mobility_Report.csv'
3037 metro_area 1/0/T/F/TRUE/FALSE Kabul Metropolitan Area './../data/Global_Mobility_Report.csv'
3038 metro_area 1/0/T/F/TRUE/FALSE Kabul Metropolitan Area './../data/Global_Mobility_Report.csv'
3039 metro_area 1/0/T/F/TRUE/FALSE Kabul Metropolitan Area './../data/Global_Mobility_Report.csv'
3040 metro_area 1/0/T/F/TRUE/FALSE Kabul Metropolitan Area './../data/Global_Mobility_Report.csv'
.... .......... .................. ....................... ......................................
See problems(...) for more details.
cases_plus_census <- load_file("./../data/COVID-19_cases_plus_census.csv")

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────
cols(
  .default = col_double(),
  county_fips_code = col_character(),
  county_name = col_character(),
  state = col_character(),
  state_fips_code = col_character(),
  date = col_date(format = ""),
  geo_id = col_character(),
  pop_5_years_over = col_logical(),
  speak_only_english_at_home = col_logical(),
  speak_spanish_at_home = col_logical(),
  speak_spanish_at_home_low_english = col_logical(),
  pop_15_and_over = col_logical(),
  pop_never_married = col_logical(),
  pop_now_married = col_logical(),
  pop_separated = col_logical(),
  pop_widowed = col_logical(),
  pop_divorced = col_logical()
)
ℹ Use `spec()` for the full column specifications.
cols_keep <- c("county_fips_code", "confirmed_cases", "deaths", "median_income", "male_pop", "female_pop", "total_pop", "median_age", "worked_at_home", "male_65_to_66", "male_67_to_69", 
               "male_70_to_74", "male_75_to_79", "male_80_to_84",
               "male_85_and_over", "female_65_to_66", "female_67_to_69", 
               "female_70_to_74", "female_75_to_79", "female_80_to_84",
               "female_85_and_over")
subset_census <- cases_plus_census[cols_keep]

subset_census$male_elderly_pop <- subset_census %>% select(c("male_65_to_66",
                                                             "male_67_to_69", 
                                                             "male_70_to_74",
                                                             "male_75_to_79",
                                                             "male_80_to_84",
                                                             "male_85_and_over")
                                                           ) %>% rowSums()

subset_census$female_elderly_pop <- subset_census %>% select(c("female_65_to_66",
                                                             "female_67_to_69", 
                                                             "female_70_to_74",
                                                             "female_75_to_79",
                                                             "female_80_to_84",
                                                             "female_85_and_over")
                                                           ) %>% rowSums()

cols_keep <- c("county_fips_code", "confirmed_cases", "deaths", "median_income",
               "male_pop", "female_pop", "total_pop", "median_age",
               "worked_at_home", "male_elderly_pop", "female_elderly_pop")
subset_census <- subset_census[cols_keep]
subset_census

# cols_keep <- c("date", "retail_and_recreation_percent_change_from_baseline", "grocery_and_pharmacy_percent_change_from_baseline", "parks_percent_change_from_baseline", "transit_stations_percent_change_from_baseline", "workplaces_percent_change_from_baseline", "residential_percent_change_from_baseline")
# subset_mobility <- global_mobility_report[cols_keep]
# glo
# subset_mobility$date <- as.Date(subset_mobility$date, format="%Y-%m-%d")
# global_mobility_report
vis_miss(global_mobility_report, sort_miss = T, warn_large_data= F)

vis_miss(tx_data, sort_miss = T, warn_large_data= F)

vis_miss(subset_census, sort_miss = T, warn_large_data = F)

library(RColorBrewer)
plot_vs_county <- function(df, col_val, percentile=FALSE,
                           fips_title="county_fips_code", banks=6, 
                           legend_title="", graphic_title=""){
  # Subset for speed 
  df <- df[c(fips_title, col_val)]
  
  # Get county data
  gcounty <- ggplot2::map_data("county")
  # USA map data
  gusa <- map_data("state")
  
  if (banks > 9){
    mycolors <- colorRampPalette(brewer.pal(9, "Reds"))(banks)
  }
  
  # Format with subregions
  fipstab <-
      transmute(maps::county.fips, fips, county = sub(":.*", "", polyname)) %>%
      unique() %>%
      separate(county, c("region", "subregion"), sep = ",")
  
  # Combine in desired order (NA for missing)
  gcounty <- left_join(gcounty, fipstab, c("region", "subregion"))


  dis <- df
  dis$rprop <- rank(df[col_val])
  dis$pcls <- cut(100 * percent_rank(df[col_val]), seq(0, 100, len = banks),
                        include.lowest = TRUE)

  # Missing data
  anti_join(gcounty, dis, by = c("fips" = fips_title)) %>%
    select(region, subregion) %>%
    unique()
  gcounty_pop <- left_join(gcounty, dis, by = c("fips" = fips_title))
  fill_vals <- gcounty_pop[col_val]

  # Plot
  if (legend_title == ""){
    legend_title <- col_val
  }

  if (percentile == FALSE){
    # names(gcounty_pop)[names(gcounty_pop) == col_val] <- "col_of_interest"
    plt <- ggplot(gcounty_pop) +
      geom_polygon(aes(long, lat, group = group, fill = get(col_val)),
                   color = "grey", size = 0.1, name="Percent Infected") +
      geom_polygon(aes(long, lat, group = group),
                   fill = NA, data = gusa, color = "lightgrey") +
      coord_map("bonne", parameters = 41.6) + ggthemes::theme_map()+
      scale_fill_gradient2()
       # scale_fill_gradient(low = "white", high = "red", na.value = "grey")
      # scale_fill_gradientn(colours = terrain.colors(10))
  }

  if (percentile == TRUE){
    plt <- ggplot(gcounty_pop) +
      geom_polygon(aes(long, lat, group = group, fill = pcls),
                   color = "grey", size = 0.1) +
      geom_polygon(aes(long, lat, group = group),
                   fill = NA, data = gusa, color = "lightgrey") +
      coord_map("bonne", parameters = 41.6) + ggthemes::theme_map() +
      scale_fill_manual(values = mycolors, na.value = "grey") +
      # scale_fill_brewer(palette = "viridis", na.value = "grey") +
      theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
            legend.background = element_rect(fill = NA), 
            legend.position = "left")
  }
  plt <- plt + labs(fill=legend_title) + ggtitle(graphic_title)
  plt
}
subset_census
subset_census['pct_infected'] <- subset_census['confirmed_cases']/subset_census['total_pop']
subset_census['pct_deaths'] <- subset_census['deaths']/subset_census['total_pop']
subset_census$death_rate <- subset_census$deaths/subset_census$confirmed_cases
subset_census$county_fips_code <-as.integer(subset_census$county_fips_code)
subset_census
plot_vs_county(subset_census, "pct_infected", legend_title = "Percent Infected")
Ignoring unknown parameters: name

plot_vs_county(subset_census, "pct_infected", percentile = TRUE, banks=11, 
               legend_title = "Percentile Infected",
               graphic_title = "Percentile of Percentage of People Infected by County")

plot_vs_county(subset_census, "pct_deaths", percentile = TRUE, banks=11, 
               legend_title = "Percentile Deaths",
               graphic_title = "Percentile of Percentage of Deaths by County")

plot_vs_county(subset_census, "death_rate", percentile = TRUE, banks=11, 
               legend_title = "Percentile Mortality Rate",
               graphic_title = "Percentile of Mortality Rate by County")

plot_vs_county(subset_census, "death_rate", 
               legend_title = "Percentile Mortality Rate",
               graphic_title = "Percentile of Mortality Rate by County")
Ignoring unknown parameters: name

census_corr_cols <- colnames(subset_census)
census_corr_cols <- census_corr_cols[-1]
census_corr_cols
 [1] "confirmed_cases"    "deaths"             "median_income"      "male_pop"           "female_pop"        
 [6] "total_pop"          "median_age"         "worked_at_home"     "male_elderly_pop"   "female_elderly_pop"
[11] "pct_infected"       "pct_deaths"         "death_rate"        
subset_census
# png("./test.png", width=800, height = 800)
census_corr_cols <- colnames(subset_census)
census_corr_cols <- census_corr_cols[-1]
ggcorr(subset_census[census_corr_cols], low="red", mid="grey", high="blue", hjust= .9, size=3, 
       label = TRUE, label_size = 3, label_color = "white", layout.exp = 2) +
  ggplot2::labs(title = "Pearson Correlation of Important Variables")

ggsave("./../imgs/census_pearson.png")
Saving 7.29 x 4.51 in image

# dev.off()
global_mobility_report
country_date_pct_change <- global_mobility_report %>% select(country_region_code
                                                             | contains("date") 
                                                             | contains("percent"))
country_date_pct_change
coi_downsampled <- country_date_pct_change %>% filter(country_region_code %in% 
                                                        c("US", "CA", "NZ", "AE", "CN", "DE", "JP")) %>% 
  filter(weekdays(date) == "Monday") %>% group_by(country_region_code, date) %>% summarise_all(mean, na.rm = T) %>% arrange(country_region_code, date)
coi_downsampled
interested_cols <- c("retail_and_recreation_percent_change_from_baseline",
                     "grocery_and_pharmacy_percent_change_from_baseline",
                     "parks_percent_change_from_baseline",
                     "transit_stations_percent_change_from_baseline",
                     "workplaces_percent_change_from_baseline",
                     "residential_percent_change_from_baseline")

col_labels <- c("Average Retail and Recreation Percent Change from Baseline",
                     "Average Grocery and Pharmacy Percent Change from Baseline",
                     "Average Parks Percent Change from Baseline",
                     "Average Transit Stations Percent Change from Baseline",
                     "Average Workplaces Percent Change from Baseline",
                     "Average Residential Percent Change from Baseline")


for (i in 1:length(col_labels)){
  print(i)
  plt <- ggplot(coi_downsampled,
       aes(x=date, y=get(interested_cols[i]), group=country_region_code,
                            color=country_region_code))+
  geom_point(aes(y=rollmean(get(interested_cols[i]), k=10, na.pad=TRUE)), size=.5)+
  geom_line(aes(y=rollmean(get(interested_cols[i]), k=10, na.pad=TRUE)))+
  # geom_point(size=.5)+geom_line()+
  labs(y = col_labels[i], x = "Date", color = "Country")+
  theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
        axis.title.y = element_text(size = 8))
  save_path <- paste(c("./../imgs/", interested_cols[i], ".png"), collapse = "")
  ggsave(save_path)
  print(plt)
}
[1] 1
Saving 7 x 7 in image
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6

# ggplot(coi_downsampled,
#        aes(x=date, y=grocery_and_pharmacy_percent_change_from_baseline, group=country_region_code,
#                             color=country_region_code))+
#   geom_point(aes(y=rollmean(retail_and_recreation_percent_change_from_baseline, k=10, na.pad=TRUE)), size=.5)+
#   geom_line(aes(y=rollmean(retail_and_recreation_percent_change_from_baseline, k=10, na.pad=TRUE)))+
#   # geom_point(size=.5)+geom_line()+
#   labs(y = "Average Grocery and Pharmacy Percent Change from Baseline", x = "Date", color = "Country")+
#   theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
#         axis.title.y = element_text(size = 8))
#   # geom_line()
tx_county_data <- tx_data %>% filter(county_name != "Statewide Unallocated")
tx_total_state_data <- tx_data %>% filter(county_name == "Statewide Unallocated")
tx_total_state_data$cumulative_deaths <- cumsum(tx_total_state_data$deaths)
tx_total_state_data$cumulative_cases <- cumsum(tx_total_state_data$confirmed_cases)
tx_county_data
tx_total_state_data

Since the above doesn’t really make sense (only 78 confirmed cases with over a thousand deaths), I am going to analyze on a per county basis and maybe that data will be more clear.

tx_by_day_based_on_county <- tx_county_data %>% 
  select(date, confirmed_cases, deaths) %>%
  group_by(date) %>% 
  summarise_all(sum, na.rm = T) %>%
  arrange(date)

# interested_cols <- c("confirmed_cases", "deaths")
# col_labels <- c("Total Cases", "Total Deaths")
# 
# for (i in 1:length(col_labels)){
#   print(i)
#   title <- paste(c("Texas ", col_labels[i]), collapse = "")
#   plt <- ggplot(tx_by_day_based_on_county,
#        aes(x=date, y=get(interested_cols[i])))+
#   # geom_point(aes(y=rollmean(get(interested_cols[i]), k=10, na.pad=TRUE)), size=.5)+
#   # geom_line(aes(y=rollmean(get(interested_cols[i]), k=10, na.pad=TRUE)))+
#   # geom_point(size=.5)+geom_line()+
#     geom_line(color)+
#   labs(y = col_labels[i], x = "Date", title = title)
#   # theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
#   #       axis.title.y = element_text(size = 8))
#   # save_path <- paste(c("./../imgs/", interested_cols[i], ".png"), collapse = "")
#   # ggsave(save_path)
#   print(plt)
# }
tx_by_day_based_on_county
install.packages("ggrepel")
trying URL 'https://cran.rstudio.com/bin/macosx/contrib/4.0/ggrepel_0.9.1.tgz'
Content type 'application/x-gzip' length 702306 bytes (685 KB)
==================================================
downloaded 685 KB

The downloaded binary packages are in
    /var/folders/2t/zk2m3vcj2_51x1r2p3cbnt9r0000gn/T//RtmpNDvNE3/downloaded_packages
library(ggrepel)
tx_by_day_based_on_county %>% pivot_longer(!date, names_to = "type", values_to = "count")
# t <- tibble("Max" = c(34378, 2248927), "date" = c("2021-01-25", "2021-01-25"),
#             "type" = c("deaths", "confirmed_cases"))
# t$date <- as.Date(t$date)
# t 

data_ends <- tx_by_day_based_on_county %>% pivot_longer(!date, names_to = "type", values_to = "count") %>% 
  group_by(type) %>% 
  top_n(1, count) 
data_ends
NA
library(scales)
# png("./../imgs/texas_covid_cases_total.png", width = 800, height = 800)
# tx_by_day_based_on_county %>% pivot_longer(!date, names_to = "type", values_to = "count")
ggplot(tx_by_day_based_on_county %>% pivot_longer(!date, names_to = "type", values_to = "count"),
       aes(x=date, y=count, group=type))+
         geom_line(aes(color=type))+
  # scale_y_continuous(trans = "log10",
  #                    labels = trans_breaks('log10', math_format(10^.x)))+
  scale_color_manual(labels = c("Confirmed Cases", "Deaths"), values = c("confirmed_cases"="blue",
                                                                         "deaths"="red"))+
  labs(y = "Total Persons", x = "Date", color = "")+
  geom_text_repel(aes(label = count), data = data_ends, size=3)+
  # scale_y_continuous(sec.axis = sec_axis(~ ., breaks = data_ends))+
  theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)))+
  ggtitle("Texas COVID-19 Cases")
  
# plt+ 
# ggplot(t, 
#              aes(x=Date, y= Max))+
#   geom_point()
# plt + geom_text_repel(aes(label = Max), data = t, fontface="plain", color="black",
#                   size=3)
# dev.off()

ggsave("./../imgs/texas_covid_cases_total.png")
Saving 7.29 x 4.51 in image

  # scale_y_log10()
         # labs(y = "Total Persons", x = "Date"))

# )
# 
# plt <- ggplot(coi_downsampled,
#        aes(x=date, y=get(interested_cols[i]), group=country_region_code,
#                             color=country_region_code))+
#   geom_point(aes(y=rollmean(get(interested_cols[i]), k=10, na.pad=TRUE)), size=.5)+
#   geom_line(aes(y=rollmean(get(interested_cols[i]), k=10, na.pad=TRUE)))+
#   # geom_point(size=.5)+geom_line()+
#   labs(y = col_labels[i], x = "Date", color = "Country")+
#   theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
#         axis.title.y = element_text(size = 8))
#   save_path <- paste(c("./../imgs/", interested_cols[i], ".png"), collapse = "")
#   ggsave(save_path)
#   print(plt)
tx_county_data
subset_census

tx_state <- map_data("state") %>% subset(region == "texas")
tx_county_map_data <- map_data("county") %>% subset(region == "texas")

tx_state
tx_county_map_data

gcounty <- map_data("county")
# 
# fipstab <-
#       transmute(maps::county.fips, fips, county = sub(":.*", "", polyname)) %>%
#       unique() %>%
#       separate(county, c("region", "subregion"), sep = ",")

# Format with subregions
fipstab <-
  transmute(maps::county.fips, fips, county = sub(":.*", "", polyname)) %>%
  unique() %>%
  separate(county, c("region", "subregion"), sep = ",")
  
  # # Combine in desired order (NA for missing)
  # gcounty <- left_join(gcounty, fipstab, c("region", "subregion"))


tx_geo_data <- left_join(gcounty, fipstab, c("region", "subregion")) %>%
  left_join(subset_census, c("fips" = "county_fips_code")) %>% filter(region == "texas") %>%
  unique()

tx_geo_data$dinfect_pcls <- cut(100 * percent_rank(tx_geo_data$pct_infected), seq(0, 100, len = 11),
                        include.lowest = TRUE)
tx_geo_data$deaths_pcls <- cut(100 * percent_rank(tx_geo_data$pct_deaths), seq(0, 100, len = 11),
                        include.lowest = TRUE)
tx_geo_data$death_rate_pcls <- cut(100 * percent_rank(tx_geo_data$death_rate), seq(0, 100, len = 11),
                        include.lowest = TRUE)

tx_geo_data


# 
# # Lowercase
# tx_county_data$county_name <- tolower(tx_county_data$county_name)
# 
# # Remove ' county'
# tx_county_data$county_name <- sub("\\s*county\\b.*", "", tx_county_data$county_name)
# tx_county_data
# 
# # Get max confirmed cases and deaths
# tx_county_data
# 
# 
# # # Join the data with state geo info
# # tx_geo_data <- left_join(tx_county_map_data, tx_county_data, by = c("subregion" = "county_name"))
# # tx_geo_data

mycolors <- colorRampPalette(brewer.pal(9, "Reds"))(11)

ggplot(tx_geo_data)+
  coord_map() + ggthemes::theme_map()+
  geom_polygon(aes(long, lat, group = group, fill=death_rate_pcls))+
  scale_fill_manual(values = mycolors, na.value = "grey") +
  theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)), legend.position = "left")+
  #       legend.background = element_rect(fill = NA),
        # legend.position = "left")+
  labs(fill="Percentile") + ggtitle("Texas County Mortality Rate Percentiles")

ggsave("./../imgs/texas_county_mortality_percentiles.png")
Saving 7.29 x 4.51 in image

# ggplot(tx_geo_data) +
#       geom_polygon(aes(long, lat, group = group, fill = death_rate_pcls),
#                    color = "grey", size = 0.1) +
#       # geom_polygon(aes(long, lat, group = group),
#       #              fill = NA, data = gusa, color = "lightgrey") +
#       coord_map("bonne", parameters = 41.6) + ggthemes::theme_map() +
#       scale_fill_manual(values = mycolors, na.value = "grey") +
#       # scale_fill_brewer(palette = "viridis", na.value = "grey") +
#       theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
#             legend.background = element_rect(fill = NA),
#             legend.position = "left")



# if (percentile == TRUE){
#     plt <- ggplot(gcounty_pop) +
#       geom_polygon(aes(long, lat, group = group, fill = pcls),
#                    color = "grey", size = 0.1) +
#       geom_polygon(aes(long, lat, group = group),
#                    fill = NA, data = gusa, color = "lightgrey") +
#       coord_map("bonne", parameters = 41.6) + ggthemes::theme_map() +
#       scale_fill_manual(values = mycolors, na.value = "grey") +
#       # scale_fill_brewer(palette = "viridis", na.value = "grey") +
#       theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
#             legend.background = element_rect(fill = NA), 
#             legend.position = "left")
    

# plt <- ggplot(gcounty_pop) +
#       geom_polygon(aes(long, lat, group = group, fill = dinfect_pcls),
#                    color = "grey", size = 0.1) +
#       geom_polygon(aes(long, lat, group = group),
#                    fill = NA, data = gusa, color = "lightgrey") +
#       coord_map("bonne", parameters = 41.6) + ggthemes::theme_map() +
#       scale_fill_manual(values = mycolors, na.value = "grey") +
#       # scale_fill_brewer(palette = "viridis", na.value = "grey") +
#       theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
#             legend.background = element_rect(fill = NA),
#             legend.position = "left")

# ggplot(tx_geo_data) +
#          coord_map() + 
#   ggthemes::theme_map()+
#   geom_polygon(aes(long, lat, group = subregion, fill = confirmed_cases))
# pc_cont_iowa <- geom_polygon(aes(long, lat, group = group, fill = pchange),
#                              color = "grey", size = 0.2)
data_ends <- dallas_data %>% pivot_longer(!date, names_to = "type", values_to = "count") %>% 
  group_by(type) %>% 
  top_n(1, count) 
data_ends
# dallas_data <- tx_county_data %>% filter(county_name == "dallas") 
dallas_data <- tx_county_data %>% filter(county_name == "Dallas County") %>% 
  select(date, confirmed_cases, deaths)

data_ends <- dallas_data %>% pivot_longer(!date, names_to = "type", values_to = "count") %>% 
  group_by(type) %>% 
  top_n(1, count) 

ggplot(dallas_data %>% pivot_longer(!date, names_to = "type", values_to = "count"),
       aes(x=date, y=count, group=type))+
         geom_line(aes(color=type))+
  # scale_y_continuous(trans = "log10", labels = trans_breaks("log10", math_format(10^.x)))+
  # scale_y_log10(breaks=c(100, 300, 500, 1000, 3000, 5000, 10000, 30000, 50000, 100000, 300000),
  #               labels=c('100', '300', '500', '1000', '3000', '5000', '10000', '30000', '50000', '100000', '300000'))+
  scale_color_manual(labels = c("Confirmed Cases", "Deaths"), values = c("confirmed_cases"="blue",
                                                                         "deaths"="red"))+
  labs(y = "Total Persons", x = "Date", color = "")+
  geom_text_repel(aes(label = count), data = data_ends, size=3)+
  theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)))+
  ggtitle("Dallas COVID-19 Cases")
  ggsave("./../imgs/dallas_covid_cases_total.png")
Saving 7.29 x 4.51 in image

sgcounty <- map_data("state")

# Format with subregions
fipstab <-
  transmute(maps::county.fips, fips, county = sub(":.*", "", polyname)) %>%
  unique() %>%
  separate(county, c("region", "subregion"), sep = ",")

us_geo_data <- left_join(gcounty, fipstab, c("region", "subregion")) %>%
  left_join(subset_census, c("fips" = "county_fips_code")) %>%
  unique() %>% select(long, lat, region, subregion, group, confirmed_cases, deaths, median_income, 
                      male_pop, female_pop, total_pop, median_age,
                      worked_at_home)
# us_geo_data

# Get the total cases by state
state_data_breakdown <- us_geo_data %>% 
  drop_na() %>% select(region, subregion, confirmed_cases, deaths, median_income, 
                      male_pop, female_pop, total_pop, median_age,
                      worked_at_home) %>% unique() %>%
  group_by(region) %>% select_if(is.numeric) %>%
  summarise_all(sum)

state_data_breakdown$pct_deaths <- state_data_breakdown$deaths/state_data_breakdown$total_pop
state_data_breakdown$pct_infect <- state_data_breakdown$confirmed_cases/state_data_breakdown$total_pop
state_data_breakdown$death_rate <- state_data_breakdown$deaths/state_data_breakdown$confirmed_cases

print(state_data_breakdown %>% arrange(death_rate, decreasing=T))

state_geo_data <- us_geo_data %>% select(long, lat, region, group) %>% left_join(state_data_breakdown,
                                                        by = "region")

state_geo_data

# ggplot(tx_by_day_based_on_county %>% pivot_longer(!date, names_to = "type", values_to = "count"),
#        aes(x=date, y=count, group=type, color = type))+
#          geom_line()+
#   scale_y_continuous(trans = "log10",
#                      labels = trans_breaks('log10', math_format(10^.x)))+
#   scale_color_manual(labels = c("Confirmed Cases", "Deaths"), values = c("confirmed_cases"="blue",
#                                                                          "deaths"="red"))+
#   labs(y = "Total Persons", x = "Date", color = "")+
#   theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)))+
#   ggtitle("US COVID-19 Cases")
  # ggsave("./../imgs/texas_covid_cases_total.png")
# state_geo_data$state_group <- factor
state_geo_data$state_group <- factor(state_geo_data$region) 
state_geo_data$state_group <- as.numeric(state_geo_data$state_group)
state_geo_data
state_data_breakdown
# http://homepage.stat.uiowa.edu/~luke/classes/STAT4580-2020/maps.html

library(scales)
sp <- select(state_data_breakdown, region = region, death_rate)
gusa <- map_data("state")

gusa_pop <- left_join(gusa, sp, "region")
gusa_pop

ggplot(gusa_pop) +
    geom_polygon(aes(long, lat, group = group, fill = death_rate), color="grey") +
    coord_map("bonne", parameters = 41.6) +
    ggthemes::theme_map()+
  theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)))+
  scale_fill_gradient(labels = percent)+
  ggtitle("Mortality Rate by State")+
  labs(fill="Mortality Rate")

ggsave("./../imgs/mortality_rate_by_state.png")
Saving 7.29 x 4.51 in image

tx_data
cases_plus_census
tx_county_data
# Get the first occurrences of covid case after first tx case
tx_first_cases_by_county <- tx_county_data %>% filter(confirmed_cases > 0) %>% 
  group_by(county_name) %>%
  arrange(date) %>% slice_min(order_by = date, n = 1) %>% 
  select(county_fips_code, county_name, date)

first_day_in_tx <- min(tx_first_cases_by_county$date)

tx_first_cases_by_county <- tx_first_cases_by_county %>% 
  mutate(days_from_first_tx_case = date - first_day_in_tx) %>%
  mutate(county_fips_code = as.integer(county_fips_code))

tx_first_cases_by_county
tx_first_cases_by_county %>% filter(days_from_first_tx_case == time_length(0, "days"))
tx_state <- map_data("state") %>% subset(region == "texas")
tx_county_map_data <- map_data("county") %>% subset(region == "texas")

gcounty <- map_data("county")

# Format with subregions
fipstab <-
  transmute(maps::county.fips, fips, county = sub(":.*", "", polyname)) %>%
  unique() %>%
  separate(county, c("region", "subregion"), sep = ",") %>% filter(region == "texas")


fipstab
tx_geo_days <- left_join(gcounty, fipstab, c("region", "subregion")) %>%
  left_join(tx_first_cases_by_county, c("fips" = "county_fips_code")) %>% filter(region == "texas") %>%
  unique()

tx_geo_days
# 
# tx_geo_data$dinfect_pcls <- cut(100 * percent_rank(tx_geo_data$pct_infected), seq(0, 100, len = 11),
#                         include.lowest = TRUE)
# tx_geo_data$deaths_pcls <- cut(100 * percent_rank(tx_geo_data$pct_deaths), seq(0, 100, len = 11),
#                         include.lowest = TRUE)
# tx_geo_data$death_rate_pcls <- cut(100 * percent_rank(tx_geo_data$death_rate), seq(0, 100, len = 11),
#                         include.lowest = TRUE)
# 
# tx_geo_data

# # Lowercase
# tx_county_data$county_name <- tolower(tx_county_data$county_name)
# 
# # Remove ' county'
# tx_county_data$county_name <- sub("\\s*county\\b.*", "", tx_county_data$county_name)
# tx_county_data
ggplot(tx_geo_days %>% mutate(days_from_first_tx_case = as.integer(days_from_first_tx_case))) +
  coord_map() + ggthemes::theme_map()+
  geom_polygon(aes(long, lat, group = group, fill=days_from_first_tx_case))+
  # scale_fill_manual(values = mycolors, na.value = "grey") +
  theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)), legend.position = "left")+
  #       legend.background = element_rect(fill = NA),
        # legend.position = "left")+
  labs(fill="Days from First TX Case") + ggtitle("Texas Spread of COVID-19")+
  # scale_fill_manual(low="red", mid="grey", high="blue")
  scale_fill_gradient(low = "grey", high = "red", na.value = NA)
ggsave("./../imgs/tx_spread_days.png")
Saving 7.29 x 4.51 in image

  # scale_fill_gradient(low="red", mid="grey", high="blue")
  # scale_fill_gradientn()
  # scale_colour_gradient2()
  
---
title: "Cleaned Notebook"
output: html_notebook
---

```{r}
library(beeswarm)
library(naniar)
library(zoo)
# install.packages("zoo")
library(janitor)
library(dplyr)
# install.packages("GGally")
# library(sets)
library(tidyverse)
library(ggplot2)
library(GGally) # for ggpairs
library(lubridate)
# install.packages("maps")
# library(maps)
```

```{r}
load_file <- function(file_path){
  read_csv(file_path)
}

tx_data <- load_file("./../data/COVID-19_cases_TX.csv")
global_mobility_report <- load_file("./../data/Global_Mobility_Report.csv")
cases_plus_census <- load_file("./../data/COVID-19_cases_plus_census.csv")
```
```{r}
cols_keep <- c("county_fips_code", "confirmed_cases", "deaths", "median_income", "male_pop", "female_pop", "total_pop", "median_age", "worked_at_home", "male_65_to_66", "male_67_to_69", 
               "male_70_to_74", "male_75_to_79", "male_80_to_84",
               "male_85_and_over", "female_65_to_66", "female_67_to_69", 
               "female_70_to_74", "female_75_to_79", "female_80_to_84",
               "female_85_and_over")
subset_census <- cases_plus_census[cols_keep]

subset_census$male_elderly_pop <- subset_census %>% select(c("male_65_to_66",
                                                             "male_67_to_69", 
                                                             "male_70_to_74",
                                                             "male_75_to_79",
                                                             "male_80_to_84",
                                                             "male_85_and_over")
                                                           ) %>% rowSums()

subset_census$female_elderly_pop <- subset_census %>% select(c("female_65_to_66",
                                                             "female_67_to_69", 
                                                             "female_70_to_74",
                                                             "female_75_to_79",
                                                             "female_80_to_84",
                                                             "female_85_and_over")
                                                           ) %>% rowSums()

cols_keep <- c("county_fips_code", "confirmed_cases", "deaths", "median_income",
               "male_pop", "female_pop", "total_pop", "median_age",
               "worked_at_home", "male_elderly_pop", "female_elderly_pop")
subset_census <- subset_census[cols_keep]
subset_census

# cols_keep <- c("date", "retail_and_recreation_percent_change_from_baseline", "grocery_and_pharmacy_percent_change_from_baseline", "parks_percent_change_from_baseline", "transit_stations_percent_change_from_baseline", "workplaces_percent_change_from_baseline", "residential_percent_change_from_baseline")
# subset_mobility <- global_mobility_report[cols_keep]
# glo
# subset_mobility$date <- as.Date(subset_mobility$date, format="%Y-%m-%d")
```

```{r}
# global_mobility_report
vis_miss(global_mobility_report, sort_miss = T, warn_large_data= F)
```


```{r}
vis_miss(tx_data, sort_miss = T, warn_large_data= F)
vis_miss(subset_census, sort_miss = T, warn_large_data = F)
```

```{r}
library(RColorBrewer)
plot_vs_county <- function(df, col_val, percentile=FALSE,
                           fips_title="county_fips_code", banks=6, 
                           legend_title="", graphic_title=""){
  # Subset for speed 
  df <- df[c(fips_title, col_val)]
  
  # Get county data
  gcounty <- ggplot2::map_data("county")
  # USA map data
  gusa <- map_data("state")
  
  if (banks > 9){
    mycolors <- colorRampPalette(brewer.pal(9, "Reds"))(banks)
  }
  
  # Format with subregions
  fipstab <-
      transmute(maps::county.fips, fips, county = sub(":.*", "", polyname)) %>%
      unique() %>%
      separate(county, c("region", "subregion"), sep = ",")
  
  # Combine in desired order (NA for missing)
  gcounty <- left_join(gcounty, fipstab, c("region", "subregion"))


  dis <- df
  dis$rprop <- rank(df[col_val])
  dis$pcls <- cut(100 * percent_rank(df[col_val]), seq(0, 100, len = banks),
                        include.lowest = TRUE)

  # Missing data
  anti_join(gcounty, dis, by = c("fips" = fips_title)) %>%
    select(region, subregion) %>%
    unique()
  gcounty_pop <- left_join(gcounty, dis, by = c("fips" = fips_title))
  fill_vals <- gcounty_pop[col_val]

  # Plot
  if (legend_title == ""){
    legend_title <- col_val
  }

  if (percentile == FALSE){
    # names(gcounty_pop)[names(gcounty_pop) == col_val] <- "col_of_interest"
    plt <- ggplot(gcounty_pop) +
      geom_polygon(aes(long, lat, group = group, fill = get(col_val)),
                   color = "grey", size = 0.1, name="Percent Infected") +
      geom_polygon(aes(long, lat, group = group),
                   fill = NA, data = gusa, color = "lightgrey") +
      coord_map("bonne", parameters = 41.6) + ggthemes::theme_map()+
      scale_fill_gradient2()
       # scale_fill_gradient(low = "white", high = "red", na.value = "grey")
      # scale_fill_gradientn(colours = terrain.colors(10))
  }

  if (percentile == TRUE){
    plt <- ggplot(gcounty_pop) +
      geom_polygon(aes(long, lat, group = group, fill = pcls),
                   color = "grey", size = 0.1) +
      geom_polygon(aes(long, lat, group = group),
                   fill = NA, data = gusa, color = "lightgrey") +
      coord_map("bonne", parameters = 41.6) + ggthemes::theme_map() +
      scale_fill_manual(values = mycolors, na.value = "grey") +
      # scale_fill_brewer(palette = "viridis", na.value = "grey") +
      theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
            legend.background = element_rect(fill = NA), 
            legend.position = "left")
  }
  plt <- plt + labs(fill=legend_title) + ggtitle(graphic_title)
  plt
}
```

```{r}
subset_census
```

```{r}
subset_census['pct_infected'] <- subset_census['confirmed_cases']/subset_census['total_pop']
subset_census['pct_deaths'] <- subset_census['deaths']/subset_census['total_pop']
subset_census$death_rate <- subset_census$deaths/subset_census$confirmed_cases
subset_census$county_fips_code <-as.integer(subset_census$county_fips_code)
subset_census
```
```{r}
plot_vs_county(subset_census, "pct_infected", legend_title = "Percent Infected")
plot_vs_county(subset_census, "pct_infected", percentile = TRUE, banks=11, 
               legend_title = "Percentile Infected",
               graphic_title = "Percentile of Percentage of People Infected by County")
plot_vs_county(subset_census, "pct_deaths", percentile = TRUE, banks=11, 
               legend_title = "Percentile Deaths",
               graphic_title = "Percentile of Percentage of Deaths by County")
plot_vs_county(subset_census, "death_rate", percentile = TRUE, banks=11, 
               legend_title = "Percentile Mortality Rate",
               graphic_title = "Percentile of Mortality Rate by County")
plot_vs_county(subset_census, "death_rate", 
               legend_title = "Percentile Mortality Rate",
               graphic_title = "Percentile of Mortality Rate by County")
```
```{r}
census_corr_cols <- colnames(subset_census)
census_corr_cols <- census_corr_cols[-1]
census_corr_cols
```

```{r}
subset_census
```

```{r}
# png("./test.png", width=800, height = 800)
census_corr_cols <- colnames(subset_census)
census_corr_cols <- census_corr_cols[-1]
ggcorr(subset_census[census_corr_cols], low="red", mid="grey", high="blue", hjust= .9, size=3, 
       label = TRUE, label_size = 3, label_color = "white", layout.exp = 2) +
  ggplot2::labs(title = "Pearson Correlation of Important Variables")

ggsave("./../imgs/census_pearson.png")

# dev.off()
```
```{r}
global_mobility_report
```


```{r}
country_date_pct_change <- global_mobility_report %>% select(country_region_code
                                                             | contains("date") 
                                                             | contains("percent"))
country_date_pct_change
```
```{r}
coi_downsampled <- country_date_pct_change %>% filter(country_region_code %in% 
                                                        c("US", "CA", "NZ", "AE", "CN", "DE", "JP")) %>% 
  filter(weekdays(date) == "Monday") %>% group_by(country_region_code, date) %>% summarise_all(mean, na.rm = T) %>% arrange(country_region_code, date)
coi_downsampled
```



```{r}
interested_cols <- c("retail_and_recreation_percent_change_from_baseline",
                     "grocery_and_pharmacy_percent_change_from_baseline",
                     "parks_percent_change_from_baseline",
                     "transit_stations_percent_change_from_baseline",
                     "workplaces_percent_change_from_baseline",
                     "residential_percent_change_from_baseline")

col_labels <- c("Average Retail and Recreation Percent Change from Baseline",
                     "Average Grocery and Pharmacy Percent Change from Baseline",
                     "Average Parks Percent Change from Baseline",
                     "Average Transit Stations Percent Change from Baseline",
                     "Average Workplaces Percent Change from Baseline",
                     "Average Residential Percent Change from Baseline")


for (i in 1:length(col_labels)){
  print(i)
  plt <- ggplot(coi_downsampled,
       aes(x=date, y=get(interested_cols[i]), group=country_region_code,
                            color=country_region_code))+
  geom_point(aes(y=rollmean(get(interested_cols[i]), k=10, na.pad=TRUE)), size=.5)+
  geom_line(aes(y=rollmean(get(interested_cols[i]), k=10, na.pad=TRUE)))+
  # geom_point(size=.5)+geom_line()+
  labs(y = col_labels[i], x = "Date", color = "Country")+
  theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
        axis.title.y = element_text(size = 8))
  save_path <- paste(c("./../imgs/", interested_cols[i], ".png"), collapse = "")
  ggsave(save_path)
  print(plt)
}
# ggplot(coi_downsampled,
#        aes(x=date, y=grocery_and_pharmacy_percent_change_from_baseline, group=country_region_code,
#                             color=country_region_code))+
  # geom_point(aes(y=rollmean(retail_and_recreation_percent_change_from_baseline, k=10, na.pad=TRUE)), size=.5)+
#   geom_line(aes(y=rollmean(retail_and_recreation_percent_change_from_baseline, k=10, na.pad=TRUE)))+
#   # geom_point(size=.5)+geom_line()+
#   labs(y = "Average Grocery and Pharmacy Percent Change from Baseline", x = "Date", color = "Country")+
#   theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
#         axis.title.y = element_text(size = 8))
#   # geom_line()
```

```{r}
tx_county_data <- tx_data %>% filter(county_name != "Statewide Unallocated")
tx_total_state_data <- tx_data %>% filter(county_name == "Statewide Unallocated")
tx_total_state_data$cumulative_deaths <- cumsum(tx_total_state_data$deaths)
tx_total_state_data$cumulative_cases <- cumsum(tx_total_state_data$confirmed_cases)
tx_county_data
tx_total_state_data
```

```{r}

```


Since the above doesn't really make sense (only 78 confirmed cases with over a thousand deaths), I am going to analyze on a per county basis and maybe that data will be more clear.

```{r}
tx_by_day_based_on_county <- tx_county_data %>% 
  select(date, confirmed_cases, deaths) %>%
  group_by(date) %>% 
  summarise_all(sum, na.rm = T) %>%
  arrange(date)

# interested_cols <- c("confirmed_cases", "deaths")
# col_labels <- c("Total Cases", "Total Deaths")
# 
# for (i in 1:length(col_labels)){
#   print(i)
#   title <- paste(c("Texas ", col_labels[i]), collapse = "")
#   plt <- ggplot(tx_by_day_based_on_county,
#        aes(x=date, y=get(interested_cols[i])))+
#   # geom_point(aes(y=rollmean(get(interested_cols[i]), k=10, na.pad=TRUE)), size=.5)+
#   # geom_line(aes(y=rollmean(get(interested_cols[i]), k=10, na.pad=TRUE)))+
#   # geom_point(size=.5)+geom_line()+
#     geom_line(color)+
#   labs(y = col_labels[i], x = "Date", title = title)
#   # theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
#   #       axis.title.y = element_text(size = 8))
#   # save_path <- paste(c("./../imgs/", interested_cols[i], ".png"), collapse = "")
#   # ggsave(save_path)
#   print(plt)
# }

```
```{r}
tx_by_day_based_on_county
```

```{r}
install.packages("ggrepel")
library(ggrepel)
```
```{r}
tx_by_day_based_on_county %>% pivot_longer(!date, names_to = "type", values_to = "count")
```


```{r}
# t <- tibble("Max" = c(34378, 2248927), "date" = c("2021-01-25", "2021-01-25"),
#             "type" = c("deaths", "confirmed_cases"))
# t$date <- as.Date(t$date)
# t 

data_ends <- tx_by_day_based_on_county %>% pivot_longer(!date, names_to = "type", values_to = "count") %>% 
  group_by(type) %>% 
  top_n(1, count) 
data_ends

```



```{r}
library(scales)
# png("./../imgs/texas_covid_cases_total.png", width = 800, height = 800)
# tx_by_day_based_on_county %>% pivot_longer(!date, names_to = "type", values_to = "count")
ggplot(tx_by_day_based_on_county %>% pivot_longer(!date, names_to = "type", values_to = "count"),
       aes(x=date, y=count, group=type))+
         geom_line(aes(color=type))+
  # scale_y_continuous(trans = "log10",
  #                    labels = trans_breaks('log10', math_format(10^.x)))+
  scale_color_manual(labels = c("Confirmed Cases", "Deaths"), values = c("confirmed_cases"="blue",
                                                                         "deaths"="red"))+
  labs(y = "Total Persons", x = "Date", color = "")+
  geom_text_repel(aes(label = count), data = data_ends, size=3)+
  # scale_y_continuous(sec.axis = sec_axis(~ ., breaks = data_ends))+
  theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)))+
  ggtitle("Texas COVID-19 Cases")
  
# plt+ 
# ggplot(t, 
#              aes(x=Date, y= Max))+
#   geom_point()
# plt + geom_text_repel(aes(label = Max), data = t, fontface="plain", color="black",
#                   size=3)
# dev.off()

ggsave("./../imgs/texas_covid_cases_total.png")
  # scale_y_log10()
         # labs(y = "Total Persons", x = "Date"))

# )
# 
# plt <- ggplot(coi_downsampled,
#        aes(x=date, y=get(interested_cols[i]), group=country_region_code,
#                             color=country_region_code))+
#   geom_point(aes(y=rollmean(get(interested_cols[i]), k=10, na.pad=TRUE)), size=.5)+
#   geom_line(aes(y=rollmean(get(interested_cols[i]), k=10, na.pad=TRUE)))+
#   # geom_point(size=.5)+geom_line()+
#   labs(y = col_labels[i], x = "Date", color = "Country")+
#   theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
#         axis.title.y = element_text(size = 8))
#   save_path <- paste(c("./../imgs/", interested_cols[i], ".png"), collapse = "")
#   ggsave(save_path)
#   print(plt)
```

```{r}
tx_county_data
```


```{r}
subset_census

tx_state <- map_data("state") %>% subset(region == "texas")
tx_county_map_data <- map_data("county") %>% subset(region == "texas")

tx_state
tx_county_map_data

gcounty <- map_data("county")
# 
# fipstab <-
#       transmute(maps::county.fips, fips, county = sub(":.*", "", polyname)) %>%
#       unique() %>%
#       separate(county, c("region", "subregion"), sep = ",")

# Format with subregions
fipstab <-
  transmute(maps::county.fips, fips, county = sub(":.*", "", polyname)) %>%
  unique() %>%
  separate(county, c("region", "subregion"), sep = ",")
  
  # # Combine in desired order (NA for missing)
  # gcounty <- left_join(gcounty, fipstab, c("region", "subregion"))


tx_geo_data <- left_join(gcounty, fipstab, c("region", "subregion")) %>%
  left_join(subset_census, c("fips" = "county_fips_code")) %>% filter(region == "texas") %>%
  unique()

tx_geo_data$dinfect_pcls <- cut(100 * percent_rank(tx_geo_data$pct_infected), seq(0, 100, len = 11),
                        include.lowest = TRUE)
tx_geo_data$deaths_pcls <- cut(100 * percent_rank(tx_geo_data$pct_deaths), seq(0, 100, len = 11),
                        include.lowest = TRUE)
tx_geo_data$death_rate_pcls <- cut(100 * percent_rank(tx_geo_data$death_rate), seq(0, 100, len = 11),
                        include.lowest = TRUE)

tx_geo_data


# 
# # Lowercase
# tx_county_data$county_name <- tolower(tx_county_data$county_name)
# 
# # Remove ' county'
# tx_county_data$county_name <- sub("\\s*county\\b.*", "", tx_county_data$county_name)
# tx_county_data
# 
# # Get max confirmed cases and deaths
# tx_county_data
# 
# 
# # # Join the data with state geo info
# # tx_geo_data <- left_join(tx_county_map_data, tx_county_data, by = c("subregion" = "county_name"))
# # tx_geo_data
```
```{r}

mycolors <- colorRampPalette(brewer.pal(9, "Reds"))(11)

ggplot(tx_geo_data)+
  coord_map() + ggthemes::theme_map()+
  geom_polygon(aes(long, lat, group = group, fill=death_rate_pcls))+
  scale_fill_manual(values = mycolors, na.value = "grey") +
  theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)), legend.position = "left")+
  #       legend.background = element_rect(fill = NA),
        # legend.position = "left")+
  labs(fill="Percentile") + ggtitle("Texas County Mortality Rate Percentiles")

ggsave("./../imgs/texas_county_mortality_percentiles.png")

# ggplot(tx_geo_data) +
#       geom_polygon(aes(long, lat, group = group, fill = death_rate_pcls),
#                    color = "grey", size = 0.1) +
#       # geom_polygon(aes(long, lat, group = group),
#       #              fill = NA, data = gusa, color = "lightgrey") +
#       coord_map("bonne", parameters = 41.6) + ggthemes::theme_map() +
#       scale_fill_manual(values = mycolors, na.value = "grey") +
#       # scale_fill_brewer(palette = "viridis", na.value = "grey") +
#       theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
#             legend.background = element_rect(fill = NA),
#             legend.position = "left")



# if (percentile == TRUE){
#     plt <- ggplot(gcounty_pop) +
#       geom_polygon(aes(long, lat, group = group, fill = pcls),
#                    color = "grey", size = 0.1) +
#       geom_polygon(aes(long, lat, group = group),
#                    fill = NA, data = gusa, color = "lightgrey") +
#       coord_map("bonne", parameters = 41.6) + ggthemes::theme_map() +
#       scale_fill_manual(values = mycolors, na.value = "grey") +
#       # scale_fill_brewer(palette = "viridis", na.value = "grey") +
#       theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
#             legend.background = element_rect(fill = NA), 
#             legend.position = "left")
    

# plt <- ggplot(gcounty_pop) +
#       geom_polygon(aes(long, lat, group = group, fill = dinfect_pcls),
#                    color = "grey", size = 0.1) +
#       geom_polygon(aes(long, lat, group = group),
#                    fill = NA, data = gusa, color = "lightgrey") +
#       coord_map("bonne", parameters = 41.6) + ggthemes::theme_map() +
#       scale_fill_manual(values = mycolors, na.value = "grey") +
#       # scale_fill_brewer(palette = "viridis", na.value = "grey") +
#       theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)),
#             legend.background = element_rect(fill = NA),
#             legend.position = "left")

# ggplot(tx_geo_data) +
#          coord_map() + 
#   ggthemes::theme_map()+
#   geom_polygon(aes(long, lat, group = subregion, fill = confirmed_cases))
# pc_cont_iowa <- geom_polygon(aes(long, lat, group = group, fill = pchange),
#                              color = "grey", size = 0.2)
```
```{r}
data_ends <- dallas_data %>% pivot_longer(!date, names_to = "type", values_to = "count") %>% 
  group_by(type) %>% 
  top_n(1, count) 
data_ends
```

```{r}
# dallas_data <- tx_county_data %>% filter(county_name == "dallas") 
dallas_data <- tx_county_data %>% filter(county_name == "Dallas County") %>% 
  select(date, confirmed_cases, deaths)

data_ends <- dallas_data %>% pivot_longer(!date, names_to = "type", values_to = "count") %>% 
  group_by(type) %>% 
  top_n(1, count) 

ggplot(dallas_data %>% pivot_longer(!date, names_to = "type", values_to = "count"),
       aes(x=date, y=count, group=type))+
         geom_line(aes(color=type))+
  # scale_y_continuous(trans = "log10", labels = trans_breaks("log10", math_format(10^.x)))+
  # scale_y_log10(breaks=c(100, 300, 500, 1000, 3000, 5000, 10000, 30000, 50000, 100000, 300000),
  #               labels=c('100', '300', '500', '1000', '3000', '5000', '10000', '30000', '50000', '100000', '300000'))+
  scale_color_manual(labels = c("Confirmed Cases", "Deaths"), values = c("confirmed_cases"="blue",
                                                                         "deaths"="red"))+
  labs(y = "Total Persons", x = "Date", color = "")+
  geom_text_repel(aes(label = count), data = data_ends, size=3)+
  theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)))+
  ggtitle("Dallas COVID-19 Cases")
  ggsave("./../imgs/dallas_covid_cases_total.png")
```


```{r}
sgcounty <- map_data("state")

# Format with subregions
fipstab <-
  transmute(maps::county.fips, fips, county = sub(":.*", "", polyname)) %>%
  unique() %>%
  separate(county, c("region", "subregion"), sep = ",")

us_geo_data <- left_join(gcounty, fipstab, c("region", "subregion")) %>%
  left_join(subset_census, c("fips" = "county_fips_code")) %>%
  unique() %>% select(long, lat, region, subregion, group, confirmed_cases, deaths, median_income, 
                      male_pop, female_pop, total_pop, median_age,
                      worked_at_home)
# us_geo_data

# Get the total cases by state
state_data_breakdown <- us_geo_data %>% 
  drop_na() %>% select(region, subregion, confirmed_cases, deaths, median_income, 
                      male_pop, female_pop, total_pop, median_age,
                      worked_at_home) %>% unique() %>%
  group_by(region) %>% select_if(is.numeric) %>%
  summarise_all(sum)

state_data_breakdown$pct_deaths <- state_data_breakdown$deaths/state_data_breakdown$total_pop
state_data_breakdown$pct_infect <- state_data_breakdown$confirmed_cases/state_data_breakdown$total_pop
state_data_breakdown$death_rate <- state_data_breakdown$deaths/state_data_breakdown$confirmed_cases

print(state_data_breakdown %>% arrange(death_rate, decreasing=T))

state_geo_data <- us_geo_data %>% select(long, lat, region, group) %>% left_join(state_data_breakdown,
                                                        by = "region")

state_geo_data

# ggplot(tx_by_day_based_on_county %>% pivot_longer(!date, names_to = "type", values_to = "count"),
#        aes(x=date, y=count, group=type, color = type))+
#          geom_line()+
#   scale_y_continuous(trans = "log10",
#                      labels = trans_breaks('log10', math_format(10^.x)))+
#   scale_color_manual(labels = c("Confirmed Cases", "Deaths"), values = c("confirmed_cases"="blue",
#                                                                          "deaths"="red"))+
#   labs(y = "Total Persons", x = "Date", color = "")+
#   theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)))+
#   ggtitle("US COVID-19 Cases")
  # ggsave("./../imgs/texas_covid_cases_total.png")
```

```{r}
# state_geo_data$state_group <- factor
state_geo_data$state_group <- factor(state_geo_data$region) 
state_geo_data$state_group <- as.numeric(state_geo_data$state_group)
state_geo_data
```



```{r}
state_data_breakdown
```
```{r}
# http://homepage.stat.uiowa.edu/~luke/classes/STAT4580-2020/maps.html

library(scales)
sp <- select(state_data_breakdown, region = region, death_rate)
gusa <- map_data("state")

gusa_pop <- left_join(gusa, sp, "region")
gusa_pop

ggplot(gusa_pop) +
    geom_polygon(aes(long, lat, group = group, fill = death_rate), color="grey") +
    coord_map("bonne", parameters = 41.6) +
    ggthemes::theme_map()+
  theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)))+
  scale_fill_gradient(labels = percent)+
  ggtitle("Mortality Rate by State")+
  labs(fill="Mortality Rate")

ggsave("./../imgs/mortality_rate_by_state.png")
```
```{r}
tx_data
cases_plus_census
```



```{r}
tx_county_data
```
```{r}
# Get the first occurrences of covid case after first tx case
tx_first_cases_by_county <- tx_county_data %>% filter(confirmed_cases > 0) %>% 
  group_by(county_name) %>%
  arrange(date) %>% slice_min(order_by = date, n = 1) %>% 
  select(county_fips_code, county_name, date)

first_day_in_tx <- min(tx_first_cases_by_county$date)

tx_first_cases_by_county <- tx_first_cases_by_county %>% 
  mutate(days_from_first_tx_case = date - first_day_in_tx) %>%
  mutate(county_fips_code = as.integer(county_fips_code))

tx_first_cases_by_county
```

```{r}
tx_first_cases_by_county %>% filter(days_from_first_tx_case == time_length(0, "days"))
```


```{r}
tx_state <- map_data("state") %>% subset(region == "texas")
tx_county_map_data <- map_data("county") %>% subset(region == "texas")

gcounty <- map_data("county")

# Format with subregions
fipstab <-
  transmute(maps::county.fips, fips, county = sub(":.*", "", polyname)) %>%
  unique() %>%
  separate(county, c("region", "subregion"), sep = ",") %>% filter(region == "texas")


fipstab
tx_geo_days <- left_join(gcounty, fipstab, c("region", "subregion")) %>%
  left_join(tx_first_cases_by_county, c("fips" = "county_fips_code")) %>% filter(region == "texas") %>%
  unique()

tx_geo_days
# 
# tx_geo_data$dinfect_pcls <- cut(100 * percent_rank(tx_geo_data$pct_infected), seq(0, 100, len = 11),
#                         include.lowest = TRUE)
# tx_geo_data$deaths_pcls <- cut(100 * percent_rank(tx_geo_data$pct_deaths), seq(0, 100, len = 11),
#                         include.lowest = TRUE)
# tx_geo_data$death_rate_pcls <- cut(100 * percent_rank(tx_geo_data$death_rate), seq(0, 100, len = 11),
#                         include.lowest = TRUE)
# 
# tx_geo_data

# # Lowercase
# tx_county_data$county_name <- tolower(tx_county_data$county_name)
# 
# # Remove ' county'
# tx_county_data$county_name <- sub("\\s*county\\b.*", "", tx_county_data$county_name)
# tx_county_data
```

```{r}
ggplot(tx_geo_days %>% mutate(days_from_first_tx_case = as.integer(days_from_first_tx_case))) +
  coord_map() + ggthemes::theme_map()+
  geom_polygon(aes(long, lat, group = group, fill=days_from_first_tx_case))+
  # scale_fill_manual(values = mycolors, na.value = "grey") +
  theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15)), legend.position = "left")+
  #       legend.background = element_rect(fill = NA),
        # legend.position = "left")+
  labs(fill="Days from First TX Case") + ggtitle("Texas Spread of COVID-19")+
  # scale_fill_manual(low="red", mid="grey", high="blue")
  scale_fill_gradient(low = "grey", high = "red", na.value = NA)
ggsave("./../imgs/tx_spread_days.png")
  # scale_fill_gradient(low="red", mid="grey", high="blue")
  # scale_fill_gradientn()
  # scale_colour_gradient2()
  
```
```{r}

```





